{"id":1135793,"date":"2025-01-08T21:29:07","date_gmt":"2025-01-08T13:29:07","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1135793.html"},"modified":"2025-01-08T21:29:09","modified_gmt":"2025-01-08T13:29:09","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8python%e8%ae%a1%e7%ae%97%e6%9f%90%e5%88%97%e6%9f%90%e6%95%b0%e6%8d%ae%e7%9a%84%e4%b8%aa%e6%95%b0%e5%ad%97-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1135793.html","title":{"rendered":"\u5982\u4f55\u5728python\u8ba1\u7b97\u67d0\u5217\u67d0\u6570\u636e\u7684\u4e2a\u6570\u5b57"},"content":{"rendered":"

\"\u5982\u4f55\u5728python\u8ba1\u7b97\u67d0\u5217\u67d0\u6570\u636e\u7684\u4e2a\u6570\u5b57\"<\/p>\n

\u8981\u5728Python\u4e2d\u8ba1\u7b97\u67d0\u5217\u67d0\u4e2a\u6570\u636e\u7684\u4e2a\u6570\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\uff1aPandas\u5e93\u3001Counter\u6a21\u5757\u3001\u5217\u8868\u89e3\u6790\u3002<\/strong> \u5728\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u4e2d\uff0cPandas\u5e93\u662f\u6700\u5e38\u7528\u7684\u5de5\u5177\uff0c\u56e0\u4e3a\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u8be6\u7ec6\u5c55\u5f00Pandas\u5e93\u7684\u4f7f\u7528\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001Pandas\u5e93\u7684\u5e94\u7528<\/p>\n<\/p>\n

Pandas\u662f\u4e00\u4e2a\u9ad8\u6548\u3001\u6613\u7528\u7684\u6570\u636e\u5206\u6790\u5de5\u5177\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86DataFrame\u6570\u636e\u7ed3\u6784\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3001\u5904\u7406\u3001\u5206\u6790\u7b49\u64cd\u4f5c\u3002\u8981\u8ba1\u7b97\u67d0\u5217\u67d0\u4e2a\u6570\u636e\u7684\u4e2a\u6570\uff0cPandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u5b89\u88c5\u4e0e\u5bfc\u5165Pandas\u5e93<\/h3>\n<\/p>\n

\u9996\u5148\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Pandas\u5e93\uff0c\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7136\u540e\u5728\u4ee3\u7801\u4e2d\u5bfc\u5165Pandas\u5e93\uff1a<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8bfb\u53d6\u6570\u636e<\/h3>\n<\/p>\n
Pandas\u652f\u6301\u591a\u79cd\u6570\u636e\u8bfb\u53d6\u65b9\u5f0f\uff0c\u5305\u62ec\u8bfb\u53d6CSV\u6587\u4ef6\u3001Excel\u6587\u4ef6\u3001SQL\u6570\u636e\u5e93\u7b49\u3002\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u5982\u4f55\u8bfb\u53d6CSV\u6587\u4ef6\u4e2d\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n
# \u8bfb\u53d6CSV\u6587\u4ef6<\/p>\n
df = pd.read_csv('data.csv')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8ba1\u7b97\u67d0\u5217\u67d0\u6570\u636e\u7684\u4e2a\u6570<\/h3>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2aDataFrame df<\/code>\uff0c\u5176\u4e2d\u5305\u542b\u4e00\u5217\u540d\u4e3acolumn_name<\/code>\uff0c\u6211\u4eec\u9700\u8981\u8ba1\u7b97\u5176\u4e2d\u67d0\u4e2a\u6570\u636evalue<\/code>\u7684\u51fa\u73b0\u6b21\u6570\u3002<\/p>\n<\/p>\n
\u65b9\u6cd5\u4e00\uff1a\u4f7f\u7528value_counts<\/code>\u51fd\u6570<\/h4>\n<\/p>\n
value_counts<\/code>\u51fd\u6570\u53ef\u4ee5\u7edf\u8ba1Series\u4e2d\u6bcf\u4e2a\u503c\u7684\u51fa\u73b0\u6b21\u6570\uff0c\u5e76\u8fd4\u56de\u4e00\u4e2aSeries\uff0c\u5176\u4e2d\u7d22\u5f15\u4e3a\u503c\uff0c\u6570\u636e\u4e3a\u51fa\u73b0\u6b21\u6570\u3002<\/p>\n<\/p>\n
# \u7edf\u8ba1\u67d0\u5217\u7684\u503c\u51fa\u73b0\u6b21\u6570<\/p>\n
value_counts = df['column_name'].value_counts()<\/p>\n
\u83b7\u53d6\u7279\u5b9a\u503c\u7684\u51fa\u73b0\u6b21\u6570<\/strong><\/h2>\n
count = value_counts.get('value', 0)<\/p>\n
print(f"'{value}'\u51fa\u73b0\u4e86{count}\u6b21")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u65b9\u6cd5\u4e8c\uff1a\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15<\/h4>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u76f4\u63a5\u8ba1\u7b97\u67d0\u4e2a\u503c\u7684\u51fa\u73b0\u6b21\u6570\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u67d0\u4e2a\u503c\u7684\u51fa\u73b0\u6b21\u6570<\/p>\n
count = (df['column_name'] == 'value').sum()<\/p>\n
print(f"'{value}'\u51fa\u73b0\u4e86{count}\u6b21")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u65b9\u6cd5\u4e09\uff1a\u4f7f\u7528groupby<\/code>\u548csize<\/code>\u51fd\u6570<\/h4>\n<\/p>\n
groupby<\/code>\u548csize<\/code>\u51fd\u6570\u53ef\u4ee5\u5bf9DataFrame\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u8ba1\u7b97\u6bcf\u7ec4\u7684\u5927\u5c0f\u3002<\/p>\n<\/p>\n
# \u8ba1\u7b97\u67d0\u5217\u6bcf\u4e2a\u503c\u7684\u51fa\u73b0\u6b21\u6570<\/p>\n
grouped = df.groupby('column_name').size()<\/p>\n
\u83b7\u53d6\u7279\u5b9a\u503c\u7684\u51fa\u73b0\u6b21\u6570<\/strong><\/h2>\n
count = grouped.get('value', 0)<\/p>\n
print(f"'{value}'\u51fa\u73b0\u4e86{count}\u6b21")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001Counter\u6a21\u5757\u7684\u5e94\u7528<\/p>\n<\/p>\n
Counter\u662fPython collections\u6a21\u5757\u4e2d\u7684\u4e00\u4e2a\u7c7b\uff0c\u7528\u4e8e\u8ba1\u6570\u53ef\u54c8\u5e0c\u5bf9\u8c61\u3002\u5b83\u662f\u4e00\u4e2a\u65e0\u5e8f\u7684\u96c6\u5408\uff0c\u5143\u7d20\u4f5c\u4e3a\u5b57\u5178\u7684\u952e\uff0c\u8ba1\u6570\u4f5c\u4e3a\u5b57\u5178\u7684\u503c\u3002<\/p>\n<\/p>\n
\u4f7f\u7528Counter\u7edf\u8ba1\u67d0\u5217\u67d0\u4e2a\u6570\u636e\u7684\u4e2a\u6570<\/h3>\n<\/p>\n
\u9996\u5148\u9700\u8981\u5bfc\u5165Counter\u7c7b\uff1a<\/p>\n<\/p>\n
from collections import Counter<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5217\u8868data_list<\/code>\uff0c\u5176\u4e2d\u5305\u542b\u67d0\u5217\u7684\u6240\u6709\u6570\u636e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Counter\u8fdb\u884c\u7edf\u8ba1\uff1a<\/p>\n<\/p>\n
# \u521b\u5efa\u6570\u636e\u5217\u8868<\/p>\n
data_list = df['column_name'].tolist()<\/p>\n
\u7edf\u8ba1\u51fa\u73b0\u6b21\u6570<\/strong><\/h2>\n
counter = Counter(data_list)<\/p>\n
\u83b7\u53d6\u7279\u5b9a\u503c\u7684\u51fa\u73b0\u6b21\u6570<\/strong><\/h2>\n
count = counter.get('value', 0)<\/p>\n
print(f"'{value}'\u51fa\u73b0\u4e86{count}\u6b21")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u5217\u8868\u89e3\u6790\u7684\u5e94\u7528<\/p>\n<\/p>\n
\u5217\u8868\u89e3\u6790\u662f\u4e00\u79cd\u7b80\u6d01\u7684\u7f16\u7a0b\u65b9\u5f0f\uff0c\u53ef\u4ee5\u7528\u4e8e\u751f\u6210\u5217\u8868\u3001\u5b57\u5178\u7b49\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u89e3\u6790\u8ba1\u7b97\u67d0\u5217\u67d0\u4e2a\u6570\u636e\u7684\u4e2a\u6570\u3002<\/p>\n<\/p>\n
\u4f7f\u7528\u5217\u8868\u89e3\u6790\u7edf\u8ba1\u67d0\u5217\u67d0\u4e2a\u6570\u636e\u7684\u4e2a\u6570<\/h3>\n<\/p>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5217\u8868data_list<\/code>\uff0c\u5176\u4e2d\u5305\u542b\u67d0\u5217\u7684\u6240\u6709\u6570\u636e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5217\u8868\u89e3\u6790\u8fdb\u884c\u7edf\u8ba1\uff1a<\/p>\n<\/p>\n
# \u521b\u5efa\u6570\u636e\u5217\u8868<\/p>\n
data_list = df['column_name'].tolist()<\/p>\n
\u8ba1\u7b97\u7279\u5b9a\u503c\u7684\u51fa\u73b0\u6b21\u6570<\/strong><\/h2>\n
count = sum(1 for item in data_list if item == 'value')<\/p>\n
print(f"'{value}'\u51fa\u73b0\u4e86{count}\u6b21")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u7efc\u5408\u793a\u4f8b<\/p>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4e00\u4e2a\u7efc\u5408\u793a\u4f8b\u6765\u5c55\u793a\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u8ba1\u7b97\u67d0\u5217\u67d0\u4e2a\u6570\u636e\u7684\u4e2a\u6570\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2aCSV\u6587\u4ef6data.csv<\/code>\uff0c\u5176\u4e2d\u5305\u542b\u4e00\u5217\u540d\u4e3acolumn_name<\/code>\u7684\u6570\u636e\uff0c\u6211\u4eec\u9700\u8981\u8ba1\u7b97\u5176\u4e2d\u503c\u4e3avalue<\/code>\u7684\u51fa\u73b0\u6b21\u6570\u3002<\/p>\n<\/p>\n
import pandas as pd<\/p>\n
from collections import Counter<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
df = pd.read_csv('data.csv')<\/p>\n
\u65b9\u6cd5\u4e00\uff1a\u4f7f\u7528value_counts\u51fd\u6570<\/strong><\/h2>\n
value_counts = df['column_name'].value_counts()<\/p>\n
count_value_counts = value_counts.get('value', 0)<\/p>\n
\u65b9\u6cd5\u4e8c\uff1a\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15<\/strong><\/h2>\n
count_boolean_indexing = (df['column_name'] == 'value').sum()<\/p>\n
\u65b9\u6cd5\u4e09\uff1a\u4f7f\u7528groupby\u548csize\u51fd\u6570<\/strong><\/h2>\n
grouped = df.groupby('column_name').size()<\/p>\n
count_groupby_size = grouped.get('value', 0)<\/p>\n
\u65b9\u6cd5\u56db\uff1a\u4f7f\u7528Counter<\/strong><\/h2>\n
data_list = df['column_name'].tolist()<\/p>\n
counter = Counter(data_list)<\/p>\n
count_counter = counter.get('value', 0)<\/p>\n
\u65b9\u6cd5\u4e94\uff1a\u4f7f\u7528\u5217\u8868\u89e3\u6790<\/strong><\/h2>\n
count_list_comprehension = sum(1 for item in data_list if item == 'value')<\/p>\n
\u8f93\u51fa\u7ed3\u679c<\/strong><\/h2>\n
print(f"\u4f7f\u7528value_counts\u51fd\u6570\u8ba1\u7b97: '{value}'\u51fa\u73b0\u4e86{count_value_counts}\u6b21")<\/p>\n
print(f"\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u8ba1\u7b97: '{value}'\u51fa\u73b0\u4e86{count_boolean_indexing}\u6b21")<\/p>\n
print(f"\u4f7f\u7528groupby\u548csize\u51fd\u6570\u8ba1\u7b97: '{value}'\u51fa\u73b0\u4e86{count_groupby_size}\u6b21")<\/p>\n
print(f"\u4f7f\u7528Counter\u8ba1\u7b97: '{value}'\u51fa\u73b0\u4e86{count_counter}\u6b21")<\/p>\n
print(f"\u4f7f\u7528\u5217\u8868\u89e3\u6790\u8ba1\u7b97: '{value}'\u51fa\u73b0\u4e86{count_list_comprehension}\u6b21")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u7efc\u5408\u793a\u4f8b\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u4f7f\u7528Pandas\u5e93\u3001Counter\u6a21\u5757\u548c\u5217\u8868\u89e3\u6790\u7b49\u591a\u79cd\u65b9\u6cd5\u6765\u8ba1\u7b97\u67d0\u5217\u67d0\u4e2a\u6570\u636e\u7684\u4e2a\u6570\u3002\u8fd9\u4e9b\u65b9\u6cd5\u5404\u6709\u4f18\u7f3a\u70b9\uff0c\u9009\u62e9\u54ea\u79cd\u65b9\u6cd5\u53d6\u51b3\u4e8e\u5177\u4f53\u7684\u5e94\u7528\u573a\u666f\u548c\u4e2a\u4eba\u7684\u7f16\u7a0b\u4e60\u60ef\u3002<\/strong> \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0cPandas\u5e93\u7531\u4e8e\u5176\u9ad8\u6548\u548c\u7b80\u6d01\u7684\u7279\u70b9\uff0c\u901a\u5e38\u662f\u5904\u7406\u6570\u636e\u7684\u9996\u9009\u5de5\u5177\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u8bfb\u53d6\u6570\u636e\u5e76\u8ba1\u7b97\u7279\u5b9a\u5217\u7684\u6570\u503c\u4e2a\u6570\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u8bfb\u53d6\u6570\u636e\u6587\u4ef6\uff08\u5982CSV\u3001Excel\u7b49\uff09\uff0c\u5e76\u901a\u8fc7\u7b80\u5355\u7684\u547d\u4ee4\u8ba1\u7b97\u7279\u5b9a\u5217\u4e2d\u6570\u636e\u7684\u4e2a\u6570\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5b89\u88c5\u4e86Pandas\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7pip install pandas<\/code>\u8fdb\u884c\u5b89\u88c5\u3002\u63a5\u7740\uff0c\u4f7f\u7528pd.read_csv()<\/code>\u8bfb\u53d6\u6570\u636e\u6587\u4ef6\uff0c\u4e4b\u540e\u5229\u7528DataFrame[column_name].count()<\/code>\u65b9\u6cd5\u7edf\u8ba1\u6307\u5b9a\u5217\u7684\u6709\u6548\u6570\u636e\u4e2a\u6570\u3002<\/p>\n
\u5982\u679c\u6570\u636e\u5217\u4e2d\u6709\u7f3a\u5931\u503c\uff0c\u5982\u4f55\u8ba1\u7b97\u975e\u7a7a\u6570\u636e\u7684\u6570\u91cf\uff1f<\/strong>
\u5728\u4f7f\u7528Pandas\u65f6\uff0ccount()<\/code>\u65b9\u6cd5\u4f1a\u81ea\u52a8\u5ffd\u7565\u7f3a\u5931\u503c\uff08NaN\uff09\uff0c\u56e0\u6b64\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u5b83\u6765\u83b7\u53d6\u975e\u7a7a\u6570\u636e\u7684\u6570\u91cf\u3002\u5982\u679c\u9700\u8981\u66f4\u52a0\u8be6\u7ec6\u7684\u4fe1\u606f\uff0c\u4f8b\u5982\u8ba1\u7b97\u7279\u5b9a\u503c\u7684\u6570\u91cf\uff0c\u53ef\u4ee5\u4f7f\u7528value_counts()<\/code>\uff0c\u8fd9\u5c06\u8fd4\u56de\u8be5\u5217\u4e2d\u5404\u4e2a\u503c\u7684\u8ba1\u6570\u3002<\/p>\n
\u5728Python\u4e2d\uff0c\u5982\u4f55\u8ba1\u7b97\u67d0\u4e00\u5217\u4e2d\u7279\u5b9a\u503c\u7684\u51fa\u73b0\u6b21\u6570\uff1f<\/strong>
\u4f7f\u7528Pandas\u7684value_counts()<\/code>\u51fd\u6570\u975e\u5e38\u65b9\u4fbf\uff0c\u5b83\u4f1a\u8fd4\u56de\u8be5\u5217\u4e2d\u6240\u6709\u552f\u4e00\u503c\u7684\u8ba1\u6570\u3002\u5982\u679c\u53ea\u60f3\u83b7\u53d6\u67d0\u4e2a\u7279\u5b9a\u503c\u7684\u6570\u91cf\uff0c\u53ef\u4ee5\u901a\u8fc7\u6761\u4ef6\u8fc7\u6ee4\u6765\u5b9e\u73b0\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528df[df['column_name'] == specific_value].shape[0]<\/code>\u6765\u8ba1\u7b97\u6307\u5b9a\u503c\u7684\u51fa\u73b0\u6b21\u6570\uff0c\u8fd9\u79cd\u65b9\u6cd5\u80fd\u786e\u4fdd\u4f60\u5f97\u5230\u60f3\u8981\u7684\u7cbe\u786e\u7ed3\u679c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u5728Python\u4e2d\u8ba1\u7b97\u67d0\u5217\u67d0\u4e2a\u6570\u636e\u7684\u4e2a\u6570\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u51e0\u79cd\u65b9\u6cd5\uff1aPandas\u5e93\u3001Counter\u6a21\u5757\u3001\u5217\u8868\u89e3\u6790\u3002 […]","protected":false},"author":3,"featured_media":1135799,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1135793"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1135793"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1135793\/revisions"}],"predecessor-version":[{"id":1135801,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1135793\/revisions\/1135801"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1135799"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1135793"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1135793"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1135793"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}